Neural network committee-based sensitivity analysis strategy for geotechnical engineering problems

Abstract

Neural network usually acts as a “black box” in diverse fields to perform prediction, classification, and regression. Different from the conventional usages, neural network is herein attempted to handle factor sensitivity analysis in a geotechnical engineering system. After systematically investigating instability of employing single neural network in factor sensitivity analysis, a neural network committee (NNC)-based sensitivity analysis strategy is first algorithmically presented based on the particular mathematical ideas of weak law of large numbers in probability and optimization. Significantly, this study especially emphasizes the practical application of the NNC-based sensitivity analysis strategy to highlight the mechanism underlying in strata movement. The principal goal is to reveal the relationships among influential factors on strata movement through estimating the relative contribution of each explicative (input) variable on dependent (output) variables of strata movement. It is demonstrated that the NNC-based sensitivity analysis strategy rationally not only reveals the relative contribution of each explicative variable on dependent variables but also indicates the predictability of each dependent variable. In addition, an improved prediction model is resulted from integrating the sensitivity analysis results into neural network modeling, and it is capable of facilitating the convergence training of neural network model and advancing its prediction precision on strata movement angles. The above outcomes indicate that the NNC-based sensitivity analysis strategy provides a new paradigm of applying neural networks to deal with complex geotechnical engineering problems.